A statistical algorithm for multi-objective handover optimization under uncertainties

The mobility robustness optimization (MRO) problem in LTE self-organizing networks (SON) is a multi-objective optimization problem; it involves a set of non-convex contradicting objective functions that depend on multiple variables such as handover (HO) parameters and user mobility classes. This paper exploits the framework of stochastic processes to develop a novel method of successively choosing a sequence of multi-variate training points for multi-objective optimization. Combined with the collected statistics and a priori knowledge, the proposed method is used in the design of an efficient MRO algorithm. The performance of the algorithm is evaluated by simulations to illustrate significant improvements with respect to both HO-related ratio link failures (RLFs) and unnecessary HOs.